Ontology in Buffalo

June 6, 2013Barry Smith

Watson’s law of bioinformatics ontologies

“As the time spent discussing a particular bioinformatics topic grows longer, the probability that someone will suggest the group develops an ontology for that topic approaches 1”

http://biomickwatson.wordpress.com

Watson’s Ontology of Bioinformaticians

Top level is

bioinformatician

bioinformation bioinformationinterested in ontology not interested in ontology

• Stanford University Biomedical Informatics Research • Mayo Clinic Department of Biomedical Informatics• University at Buffalo Department of Philosophy

Three US partner institutions:

RELATION TO TIME

GRANULARITY

CONTINUANT OCCURRENT

INDEPENDENT DEPENDENT

ORGAN ANDORGANISM

Organism(NCBI

Taxonomy)

Anatomical Entity(FMA, CARO)

OrganFunction

(FMP, CPRO) Phenotypic

Quality(PaTO)

Biological Process

(GO)CELL AND CELLULAR

COMPONENT

Cell(CL)

Cellular Compone

nt(FMA, GO)

Cellular Function

(GO)

MOLECULEMolecule

(ChEBI, SO,RnaO, PrO)

Molecular Function(GO)

Molecular Process

(GO)Open Biomedical Ontologies (OBO) Foundry

(Gene Ontology marked in yellow)

© Ocean Informatics 2005 4.10

Enterprise

Comprehensive Basic

Components

EHR

Multimediagenetics

workflow

identity

Clinicalref data Clinical

models

terms

Security / access control

realtimegateway

telemedicine

HILS

otherprovider

UPDATEQUERY

demographics

guidelinesprotocolsInteractions

DSLocal

modelling

notifications

DSS

PAS

billing

portal

Alliedhealth

patientPAYER

Msg gateway

Imaging lab

ECG etc

Path lab

LAB

Secondaryusers

Online drug,Interactions DB Online

archetypes

Online terminology

Online Demographic

registries

PatientRecord

with thanks to Thomas Beale

11

Explosion of “biomedical ontology” since 1999

Biomedical Ontologies co-developed at UBBCO Biocollections OntologyBFO Basic Formal OntologyCL Cell OntologyENVO Environment Ontology FMA Foundational Model of AnatomyGO Gene OntologyIDO Infectious Disease OntologyND Neurological Disease OntologyMFO Mental Functioning OntologyNPT Neuropsychological Testing OntologyOBI Ontology for Biomedical InvestigationsOGMS Ontology for General Medical ScienceOHD Oral Health and Disease OntologyPCO Population and Community OntologyPO Plant OntologyPRO Protein Ontology

Biomedical Ontologies co-developed at UBBCO Biocollections OntologyBFO Basic Formal OntologyCL Cell OntologyENVO Environment Ontology FMA Foundational Model of AnatomyGO Gene OntologyIDO Infectious Disease OntologyND Neurological Disease OntologyMFO Mental Functioning OntologyNPT Neuropsychological Testing OntologyOBI Ontology for Biomedical InvestigationsOGMS Ontology for General Medical ScienceOHD Oral Health and Disease OntologyPCO Population and Community OntologyPO Plant OntologyPRO Protein Ontology

http://www.ifomis.org/bfo/users

Biomedical Ontologies co-developed at UBBCO Biocollections OntologyBFO Basic Formal OntologyCL Cell OntologyENVO Environment Ontology FMA Foundational Model of AnatomyGO Gene OntologyIAO Information Artifact OntologyIDO Infectious Disease OntologyND Neurological Disease OntologyMFO Mental Functioning OntologyNPT Neuropsychological Testing OntologyOBI Ontology for Biomedical InvestigationsOGMS Ontology for General Medical SciencePCO Population and Community OntologyPO Plant OntologyPRO Protein Ontology

Biomedical Ontology in Buffalo

BS, Alan Ruttenberg, Alex Diehl

PhilosophyDental School,

IHI Neurology

Werner Ceusters, Dagobert Soergel, Peter Elkin

Psychiatry, IHIDental School,

Library and Information Studies

new Chair of BiomedicalInformatics

IHI: Institute for Healthcare Informatics

IHI Ontology Machine

Strategy• using BFO, OGMS and their extension ontologies

to provide a consistent framework for the representation of the types of particulars

• developing systematic ways for the consistent tracking of particulars (patients, disorders, encounters …)

• putting these together to serve consistent representation of the assertional knowledge in the IHI repository

Strategy• using BFO, OGMS and their extension ontologies

to provide a consistent framework for the representation of the types of particulars

• developing systematic ways for the consistent tracking of particulars (patients, disorders, encounters …)

• putting these together to serve consistent representation of the assertional knowledge in the IHI repository

Acknowledgement• IDO: Immune System Biological Networks: A Case Study

in Improved Data Integration & Analysis (NIH / NIAID)• ImmPort: Bioinformatics Integration Support Contract

(NIH/NIAID)• Plant Ontology (NSF)• OPMQoL: Ontology for Pain and Related Disability,

Mental Health and Quality of Life (NIH/National Institute of Dental and Craniofacial Research)

• PRO: A Protein Ontology in Open Biomedical Ontologies (NIH/NIGMS)

• NCBO: National Center for Biomedical Ontology (NIH/NHGRI)

Further reading

National Center for Ontological Researchhttp://ncor.buffalo.edu

Contactphismith@buffalo.edu